Objective To study the seasonal distribution characteristics of polycyclic aromatic hydrocarbons (PAHs) in PM_2.5 in Lianyungang City, and analyze the sources of PAHs pollution, and to evaluate the health risks of PAHs in different seasons. Methods PM2.5 samples were collected regularly from January 2019 to December 2023, and 16 types of PAHs were determined by HPLC. Kruskal-Wallis H test was used to compare the concentrations of PM2.5 and PAHs in different years and seasons. The source of PAHs was analyzed by characteristic ratio and principal component analysis (PCA). Health risks were assessed using the BaP equivalent method and the incremental lifetime cancer risk (ILCR) model. Results The annual exceedance rates of PM_2.5 and BaP in Lianyungang showed a decreasing trend from 2019 to 2023. PM_2.5, total PAHs and PAHs monomers (except Ace, Flu and Acy) all showed significant seasonal differences, with the highest concentration in winter (P<0.001). The average proportion of 4-ring PAHs was the highest and the average proportion of 2-ring PAHs was the lowest. The proportion of 5-6 ring PAHs was relatively high in winter and spring. PM2.5and PAHs were negatively correlated with temperature, relative humidity and precipitation, and were positively correlated with atmospheric pressure. PM_2.5 was negatively correlated with wind speed, while some PAHs monomers were positively correlated with wind speed. The characteristic ratio and PCA results showed that the main sources of PAHs in Lianyungang City were mixed sources of road dust and vehicle emissions, oil pollution sources and biomass combustion sources. The results of ILCR showed that the highest risk was found in adults, with males slightly higher than females. In Lianyungang, the maximum value of ILCR in winter was more than 10-6 in people over 9 years old. Conclusion The main sources of PAHs in PM_2.5 in Lianyungang City are mixed sources of road dust and vehicle emissions, oil pollution sources, and biomass combustion sources. Under the current exposure level of PAHs in PM_2.5, residents have a certain potential carcinogenic risk.

Rhegmatogenous retinal detachment(RRD)is a vision-threatening ocular emergency with the potential risk of blindness. Pars plana vitrectomy(PPV)is the treatment of choice for RRD, especially for complex retinal detachments such as posterior pole retinal tears, grade B or higher proliferative vitreoretinopathy, and concomitant choroidal detachment. Although most patients achieve good anatomical restoration after surgery, some patients may still experience postoperative symptoms such as distorted vision, abnormal color perception, and lack of improvement in corrected visual acuity. Epiretinal membranes(ERM), as one of the most common complications after RRD, occur in 4% to 13% of cases, which not only affect the recovery of patients' postoperative vision, but also are frequently associated with visual distortion. In severe cases, a second surgical intervention may be required. In recent years, internal limiting membrane peeling(ILMP)has been introduced as an adjunctive procedure in the treatment of RRD, to facilitate retinal reorientation, reduce postoperative complications, and improve patients' postoperative visual quality and quality of life. However, previous studies on the effects of combined intraoperative ILMP on retinal anatomy and visual function recovery are limited. Therefore, this article reviews the mechanism, clinical application, challenges and research progress of ILMP in RRD, providing a reference for surgical decision making, disease assessment, and prognosis evaluation.

OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

An ideal dermal filler should integrate filling, repair, and anti-aging effects, with immediate tissue augmentation, slow degradation, and progressive stimulation of collagen regeneration. However, commonly used hyaluronic acid (HA) hydrogels, while effective for rapid filling, suffer from limited duration of support, weak cell adhesion, and an inability to promote collagen regeneration. Silk fibroin (SF), a natural protein from silkworm cocoons, is known for its excellent cell adhesion and collagen-stimulating abilities. However, its limited gelation capability restricts its potential application as a standalone injectable hydrogel. Based on a complementary strategy, this study combines the rapid gelling properties of HA with the collagen regenerative properties of SF to create a co-crosslinked HA-SF hydrogel. The composite hydrogel merges HA's rapid filling effect with SF's strong tissue adhesion and collagen-stimulating abilities. The formulation, physicochemical properties, degradation, biocompatibility, and filling effects of the HA-SF hydrogel were systematically investigated. HA-SF hydrogel exhibits excellent mechanical properties and ensures long-term support while maintaining injectability. Interestingly, after intradermal injection in the UVB-induced photoaging model, HA-SF hydrogel not only enhances hydrogel-cell interaction but also continues to stimulate collagen regeneration, especially type III collagen. This dual action achieves the biological effects of repair and anti-aging while maintaining the filling effect. Proteomic analysis confirms that repair and anti-aging effects are enhanced by the regulation of skin fibroblasts and modulation of amino acid and lipid metabolism. This composite hydrogel holds strong promise for clinical applications, offering a safer, long-lasting, and more natural injectable filler that combines filling, repair, and anti-aging into one system.

Sennoside A (SA), a typical prodrug, exerts its laxative effect only after its transformation into rheinanthrone catalyzed by gut microbial hydrolases and reductases. Hydrolases have been identified, but reductases remain unknown. By linking a photoreactive group to the SA scaffold, we synthesized a photoaffinity probe to covalently label SA reductases and identified SA reductases using activity-based protein profiling (ABPP). From lysates of an active strain, Bifidobacterium pseudocatenulatum (B. pseudocatenulatum), 397 proteins were enriched and subsequently identified using mass spectrometry (MS). Among these proteins, chromate reductase/nicotinamide adenine dinucleotide (NADH) phosphate (NADPH)-dependent flavin mononucleotide (FMN) reductase/oxygen-insensitive NADPH nitroreductase (nfrA) was identified as a potent SA reductase through further bioinformatic analysis and The Universal Protein Resource (UniProt) database screening. We also determined that recombinant nfrA could reduce SA. Our study contributes to further illuminating mechanisms of SA transformation to rheinanthrone and simultaneously offers an effective method to identify gut bacterial reductases.

  Objective  To investigate the needs of eight-year program clinical medical students for the organization and contents of clinical oncology courses.  Methods  From September to November 2020, a questionnaire survey was conducted among eight-year program clinical medicine students in Peking Union Medical College to find out their knowledge base in oncology, teaching mode preference and course contents of interest.  Results  A total of 122 students participated in the survey, in which 89.3%(109/122) of the students showed interest in basic and clinical research projects related to oncology, 84.4%(103/122) thought it was better to use Simulation-based medical education (SBME), and 91.0%(111/122) hoped to learn throughoff-line discussion. In terms of course contents, eight-year program medical students were more interested in knowledge directly related to clinical context, such as diagnosis, treatment, multidisciplinary comprehensive treatment and evidence-based medicine. In terms of sub-analysis, traditional eight-year students (86%, 92/107) showed a higher acceptance of palliative care than students in the 4+4 reform program(60%, 9/15) and were more willing to act as scriptwriters in SBME(26% vs. 7%, P=0.013). The students in clinical phase gained a better understanding of oncology knowledge through research training and were more inclined to take on the role of scriptwriters in SBME than those in basic phase (27% vs. 11%, P=0.048).  Conclusions  The eight-year program clinical medical students are interested in the clinical oncology course and prefer study in the form of Simulation-based medical education (SBME).

Objective:To investigate the diagnostic value of serum matrix metalloproteinase 3 (MMP-3) combined with anti-mutant citrulline vimentin antibody (anti-MCV) and rheumatoid factor (RF) in rheumatoid arthritis (RA).Methods:A retrospective study was conducted to select 78 patients diagnosed with RA, 30 with Sjogren′s syndrome (SS), 25 with connective tissue disease (CTD), and 38 with antiphospholipid syndrome (APS) from the Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University from August 2022 to April 2024. In the same period, 50 healthy controls were selected from the physical examination center of our hospital. Fasting peripheral blood RF, anti-streptolysin O (ASO), C-reactive protein (CRP), erythrocyte deposition rate (ESR), interleukin-6 (IL-6), procalcitonin (PCT), anti-keratin antibody (AKA), anti-MCV, MMP-3, anti-cyclic citrullinated peptide (anti-CCP) level. Fasting peripheral blood MMP-3 levels in SS, CTD, APS patients and healthy control group were compared, Spearman correlation analysis was used to evaluate the correlation between MMP-3 levels and various indicators, and logistic regression analysis was used to screen the related influencing factors of RA occurrence. The diagnostic value of serum MMP-3, anti-MCV, RF and combined detection for RA was evaluated by receiver operating characteristic (ROC) curve.Results:There were statistically significant differences in RF, CRP, ESR, IL6, AKA, antiMCV, MMP-3 and anti-CCP between RA group and healthy control group (all P<0.001), while there were no statistically significant differences in PCT and ASO between the RA group and the healthy control group (all P>0.05). Serum MMP-3 level in the RA group was significantly higher than that in SS, CTD, APS and healthy control group, and the difference was statistically significant (all P<0.05). MMP-3 was positively correlated with anti-CCP, CRP, ESR, anti-MCV, RF and AKA ( r=0.403, 0.532, 0.530, 0.431, 0.427, 0.391, all P<0.05). Multivariate logistic regression analysis showed that MMP-3 ( OR=1.082, P=0.02), anti-MCV ( OR=1.015, P=0.049) and RF ( OR=1.046, P=0.036) were independent risk factors for RA. ROC curve showed that when the cut-off value of MMP-3 was 53.10 ng/ml, the area under the curve for diagnosing RA was 0.889, the sensitivity was 74.5%, and the specificity was 96.0%. The area under ROC curve of MMP-3 combined with anti-MCV and RF for the diagnosis of RA was 0.975, the sensitivity was 88.2%, and the specificity was 99.7%. Conclusions:MMP-3 has a certain value in the differential diagnosis of RA, and MMP-3 combined with anti-MCV and RF is of great significance in the auxiliary diagnosis of RA.

Objective:To summarize long-term clinical follow-up results of segment instability between the upper instrumented vertebra (UIV) and the adjacent upper vertebra (UIV+1) and to establish the optimal timing for surgery for UIV+1.Methods:A retrospective analysis was conducted on 265 patients with lumbar degenerative diseases who underwent transforaminal lumbar interbody fusion (TLIF) surgery at the Department of Spinal Surgery, Zhongda Hospital, from January 2014 to December 2018. The cohort included 119 male and 146 female patients, with an average age of 64.93 years (range: 32-86 years). Preoperative dynamic imaging measured sagittal angulation (SA) and sagittal translation (ST) of the UIV+1/UIV segment. Patients with SA>10° or ST>2 mm were categorized into the unstable group, further divided into the unstable non-fusion group and the unstable fusion group based on whether UIV+1 expansion fusion was performed. The remaining patients were classified into the stable group. Imaging indicators, Visual Analogue Scale (VAS) scores, Oswestry disability index (ODI) scores, and Japanese Orthopaedic Association (JOA) scores were compared among the groups, with JOA improvement rates calculated to assess clinical efficacy. Pearson correlation coefficient analysis was employed to examine correlations between preoperative imaging indicators and final follow-up JOA improvement rates. Receiver Operating Characteristic (ROC) curves and the maximum Youden index were utilized to determine thresholds for preoperative SA and ST.Results:The follow-up duration for all patients was 73.53±12.92 months (range: 61-108 months). The stable group (124 cases) included 61 males and 63 females, aged 64.31±9.83 years (range: 44-82 years). The unstable non-fusion group (59 cases) included 22 males and 37 females, aged 65.76±11.01 years (range: 32-86 years). The unstable fusion group (82 cases) included 36 males and 46 females, aged 65.26±8.68 years (range: 47-80 years). At the last follow-up, the unstable non-fusion group exhibited ΔSA 0.90°±1.97° and ΔST 0.77±1.27 mm, both significantly higher than the stable group's ΔSA 0.25°±1.57° and ΔST 0.34±0.34 mm ( t=3.564, P<0.001; t=2.311, P=0.022). Clinical improvements were lower in the unstable non-fusion group compared to the other two groups: VAS (2.28±0.83), ODI (5.91%±3.46%), JOA (24.11±1.78), with a JOA improvement rate of 60%. The stable group showed VAS (1.51±0.69), ODI (3.71%±1.75%), JOA (27.33±1.91), with a JOA improvement rate of 83%. The unstable fusion group had VAS (1.46±0.83), ODI (3.46%±1.81%), JOA (26.48±1.66), with a JOA improvement rate of 78%. These differences were statistically significant ( F=32.117, P<0.001; F=24.827, P<0.001; F=92.658, P<0.001; F=93.341, P<0.001). The JOA improvement rate was negatively correlated with preoperative SA ( r=-0.363, P<0.001) to a low extent, and with preoperative ST ( r=-0.596, P<0.001) to a moderate extent. ROC curve analysis determined the preoperative SA threshold as 11.5° and the preoperative ST threshold as 1.85 mm. Conclusion:Pre-existing instability of the responsible segment UIV and UIV+1 (SA>10° or ST>2 mm) may worsen during long-term follow-up after TLIF. When preoperative SA exceeds 11.5° and ST exceeds 1.85 mm between UIV and UIV+1, performing an extended fusion involving UIV+1 can ensure surgical efficacy over long-term follow-up.

Objective:To develop and validate a predictive model of 28-day mortality in sepsis based on lactate dehydrogenase-to-albumin ratio (LAR).Methods:Sepsis patients diagnosed in the department of intensive care medicine of the First Affiliated Hospital of Soochow University from August 1, 2017 to September 1, 2022 were retrospective selected. Clinical data, laboratory indicators, disease severity scores [acute physiology and chronic health evaluation Ⅱ(APACHEⅡ), sequential organ failure assessment (SOFA)] were collected. Patients were divided into death group and survival group according to whether they died at 28 days, and the difference between the two groups was compared. The dataset was randomly divided into training set and validation set according to 7∶3. Lasso regression method was used to screen the risk factors affecting the 28-day death of sepsis patients, and incorporating multivariate Logistic regression analysis (stepwise regression) were included, a prediction model was constructed based on the independent risk factors obtained, and a nomogram was drawn. The nomogram prediction model was established. Receiver operator characteristic curve (ROC curve) was drawn to analyze and evaluate the predictive efficacy of the model. Hosmer-Lemeshow test, calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and clinical practicability of the model, respectively.Results:A total of 394 patients with sepsis were included, with 248 survivors and 146 non-survivors at 28 days. Compared with the survival group, the age, proportion of chronic obstructive pneumonia, respiratory rate, lactic acid, red blood cell distribution width, prothrombin time, activated partial thromboplastin time, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, blood potassium, blood phosphorus, LAR, SOFA score, and APACHEⅡ score in the death group were significantly increased, while oxygenation index, monocyte count, platelet count, fibrinogen, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, and blood calcium were significantly reduced. In the training set, LAR, age, oxygenation index, blood urea nitrogen, lactic acid, total cholesterol, fibrinogen, blood potassium and blood phosphorus were screened by Lasso regression. Multivariate Logistic regression analysis finally included LAR [odds ratio ( OR) = 1.029, 95% confidence interval (95% CI) was 1.014-1.047, P < 0.001], age ( OR = 1.023, 95% CI was 1.005-1.043, P = 0.012), lactic acid ( OR = 1.089, 95% CI was 1.003-1.186, P = 0.043), oxygenation index ( OR = 0.996, 95% CI was 0.993-0.998, P = 0.002), total cholesterol ( OR = 0.662, 95% CI was 0.496-0.865, P = 0.003) and blood potassium ( OR = 1.852, 95% CI was 1.169-2.996, P = 0.010). A total of 6 predictor variables were used to establish a prediction model. ROC curve showed that the area under the curve (AUC) of the model in the training set and validation set were 0.773 (95% CI was 0.715-0.831) and 0.793 (95% CI was 0.703-0.884), which was better than APACHEⅡ score (AUC were 0.699 and 0.745) and SOFA score (AUC were 0.644 and 0.650), and the cut-off values were 0.421 and 0.309, the sensitivity were 62.4% and 82.2%, and the specificity were 82.2% and 68.9%, respectively. The results of Hosmer-Lemeshow test and calibration curve showed that the predicted results of the model were in good agreement with the actual clinical observation results, and the DCA showed that the model had good clinical application value. Conclusion:The prediction model based on LAR has a good predictive value for 28-day mortality in patients with sepsis and can guide clinical decision-making.

The comprehensive detection and identification of active ingredients in complex matrices is a crucial challenge.Liquid chromatography coupled with high-resolution mass spectrometry(LC-HRMS)is the most prominent analytical platform for the exploration of novel active compounds from complex matrices.However,the LC-HRMS-based analysis workflow suffers from several bottleneck issues,such as trace content of target compounds,limited acquisition for fragment information,and uncertainty in interpreting relevant MS2 spectra.Lycibarbarspermidines are vital antioxidant active ingredients in Lycii Fructus,while the reported structures are merely focused on dicaffeoylspermidines due to their low content.To comprehensively detect the new structures of lycibarbarspermidine derivatives,a"depict"strategy was developed in this study.First,potential new lycibarbarspermidine derivatives were designed according to the biosynthetic pathway,and a comprehensive database was established,which enlarged the coverage of lycibarbarspermidine derivatives.Second,the polarity-oriented sample prep-aration of potential new compounds increased the concentration of the target compounds.Third,the construction of the molecular network based on the fragmentation pathway of lycibarbarspermidine derivatives broadened the comprehensiveness of identification.Finally,the weak response signals were captured by data-dependent scanning(DDA)followed by parallel reaction monitoring(PRM),and the efficiency of acquiring MS2 fragment ions of target compounds was significantly improved.Based on the integrated strategy above,210 lycibarbarspermidine derivatives were detected and identified from Lycii Fructus,and in particular,170 potential new compounds were structurally characterized.The integrated strategy improved the sensitivity of detection and the coverage of low-response components,and it is expected to be a promising pipeline for discovering new compounds.